Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations1300
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory182.9 KiB
Average record size in memory144.1 B

Variable types

Numeric13
Categorical5

Alerts

id is highly overall correlated with Покупательская активность and 1 other fieldsHigh correlation
Время_в_предыдущем_месяце_мин is highly overall correlated with Покупательская активностьHigh correlation
Выручка_предыдущий_месяц is highly overall correlated with Выручка_текущий_месяцHigh correlation
Выручка_текущий_месяц is highly overall correlated with Выручка_предыдущий_месяцHigh correlation
Покупательская активность is highly overall correlated with id and 2 other fieldsHigh correlation
Страниц_за_визит is highly overall correlated with id and 1 other fieldsHigh correlation
Выручка_текущий_месяц is highly skewed (γ1 = 31.77724825) Skewed
id is uniformly distributed Uniform
id has unique values Unique
Неоплаченные_продукты_штук_квартал has 116 (8.9%) zeros Zeros
Ошибка_сервиса has 17 (1.3%) zeros Zeros

Reproduction

Analysis started2024-12-18 21:35:18.296664
Analysis finished2024-12-18 21:36:29.467107
Duration1 minute and 11.17 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct1300
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215997.5
Minimum215348
Maximum216647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:29.784931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum215348
5-th percentile215412.95
Q1215672.75
median215997.5
Q3216322.25
95-th percentile216582.05
Maximum216647
Range1299
Interquartile range (IQR)649.5

Descriptive statistics

Standard deviation375.42198
Coefficient of variation (CV)0.0017380849
Kurtosis-1.2
Mean215997.5
Median Absolute Deviation (MAD)325
Skewness0
Sum2.8079675 × 108
Variance140941.67
MonotonicityStrictly increasing
2024-12-18T21:36:30.484530image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215348 1
 
0.1%
216202 1
 
0.1%
216220 1
 
0.1%
216219 1
 
0.1%
216218 1
 
0.1%
216217 1
 
0.1%
216216 1
 
0.1%
216215 1
 
0.1%
216214 1
 
0.1%
216213 1
 
0.1%
Other values (1290) 1290
99.2%
ValueCountFrequency (%)
215348 1
0.1%
215349 1
0.1%
215350 1
0.1%
215351 1
0.1%
215352 1
0.1%
215353 1
0.1%
215354 1
0.1%
215355 1
0.1%
215356 1
0.1%
215357 1
0.1%
ValueCountFrequency (%)
216647 1
0.1%
216646 1
0.1%
216645 1
0.1%
216644 1
0.1%
216643 1
0.1%
216642 1
0.1%
216641 1
0.1%
216640 1
0.1%
216639 1
0.1%
216638 1
0.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Прежний уровень
802 
Снизилась
498 

Length

Max length15
Median length15
Mean length12.701538
Min length9

Characters and Unicode

Total characters16512
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowСнизилась
2nd rowСнизилась
3rd rowСнизилась
4th rowСнизилась
5th rowСнизилась

Common Values

ValueCountFrequency (%)
Прежний уровень 802
61.7%
Снизилась 498
38.3%

Length

2024-12-18T21:36:31.276644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T21:36:31.687564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
прежний 802
38.2%
уровень 802
38.2%
снизилась 498
23.7%

Most occurring characters

ValueCountFrequency (%)
н 2102
12.7%
и 1798
10.9%
р 1604
 
9.7%
е 1604
 
9.7%
ь 1300
 
7.9%
П 802
 
4.9%
в 802
 
4.9%
о 802
 
4.9%
у 802
 
4.9%
802
 
4.9%
Other values (7) 4094
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
н 2102
12.7%
и 1798
10.9%
р 1604
 
9.7%
е 1604
 
9.7%
ь 1300
 
7.9%
П 802
 
4.9%
в 802
 
4.9%
о 802
 
4.9%
у 802
 
4.9%
802
 
4.9%
Other values (7) 4094
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
н 2102
12.7%
и 1798
10.9%
р 1604
 
9.7%
е 1604
 
9.7%
ь 1300
 
7.9%
П 802
 
4.9%
в 802
 
4.9%
о 802
 
4.9%
у 802
 
4.9%
802
 
4.9%
Other values (7) 4094
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
н 2102
12.7%
и 1798
10.9%
р 1604
 
9.7%
е 1604
 
9.7%
ь 1300
 
7.9%
П 802
 
4.9%
в 802
 
4.9%
о 802
 
4.9%
у 802
 
4.9%
802
 
4.9%
Other values (7) 4094
24.8%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
стандарт
924 
премиум
376 

Length

Max length8
Median length8
Mean length7.7107692
Min length7

Characters and Unicode

Total characters10024
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowпремиум
2nd rowпремиум
3rd rowстандарт
4th rowстандарт
5th rowстандарт

Common Values

ValueCountFrequency (%)
стандарт 924
71.1%
премиум 376
28.9%

Length

2024-12-18T21:36:32.280945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T21:36:32.754420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
стандарт 924
71.1%
премиум 376
28.9%

Most occurring characters

ValueCountFrequency (%)
т 1848
18.4%
а 1848
18.4%
р 1300
13.0%
с 924
9.2%
н 924
9.2%
д 924
9.2%
м 752
7.5%
п 376
 
3.8%
е 376
 
3.8%
и 376
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
т 1848
18.4%
а 1848
18.4%
р 1300
13.0%
с 924
9.2%
н 924
9.2%
д 924
9.2%
м 752
7.5%
п 376
 
3.8%
е 376
 
3.8%
и 376
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
т 1848
18.4%
а 1848
18.4%
р 1300
13.0%
с 924
9.2%
н 924
9.2%
д 924
9.2%
м 752
7.5%
п 376
 
3.8%
е 376
 
3.8%
и 376
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
т 1848
18.4%
а 1848
18.4%
р 1300
13.0%
с 924
9.2%
н 924
9.2%
д 924
9.2%
м 752
7.5%
п 376
 
3.8%
е 376
 
3.8%
и 376
 
3.8%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
да
962 
нет
338 

Length

Max length3
Median length2
Mean length2.26
Min length2

Characters and Unicode

Total characters2938
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowда
2nd rowда
3rd rowнет
4th rowда
5th rowнет

Common Values

ValueCountFrequency (%)
да 962
74.0%
нет 338
 
26.0%

Length

2024-12-18T21:36:33.255273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T21:36:33.668177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
да 962
74.0%
нет 338
 
26.0%

Most occurring characters

ValueCountFrequency (%)
д 962
32.7%
а 962
32.7%
н 338
 
11.5%
е 338
 
11.5%
т 338
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2938
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
д 962
32.7%
а 962
32.7%
н 338
 
11.5%
е 338
 
11.5%
т 338
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2938
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
д 962
32.7%
а 962
32.7%
н 338
 
11.5%
е 338
 
11.5%
т 338
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2938
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
д 962
32.7%
а 962
32.7%
н 338
 
11.5%
е 338
 
11.5%
т 338
 
11.5%
Distinct41
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2537692
Minimum0.9
Maximum6.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:34.086122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q13.7
median4.2
Q34.9
95-th percentile5.8
Maximum6.6
Range5.7
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.0148139
Coefficient of variation (CV)0.23856816
Kurtosis0.62060502
Mean4.2537692
Median Absolute Deviation (MAD)0.7
Skewness-0.44478178
Sum5529.9
Variance1.0298472
MonotonicityNot monotonic
2024-12-18T21:36:34.785425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
4.1 94
 
7.2%
3.9 83
 
6.4%
4.4 82
 
6.3%
4 71
 
5.5%
5.5 68
 
5.2%
4.3 66
 
5.1%
4.9 60
 
4.6%
3.5 50
 
3.8%
4.6 49
 
3.8%
3.3 47
 
3.6%
Other values (31) 630
48.5%
ValueCountFrequency (%)
0.9 11
 
0.8%
1.4 5
 
0.4%
1.5 8
 
0.6%
1.7 12
 
0.9%
2.4 42
3.2%
2.6 20
1.5%
2.7 8
 
0.6%
2.9 7
 
0.5%
3 16
 
1.2%
3.1 16
 
1.2%
ValueCountFrequency (%)
6.6 12
 
0.9%
6.3 12
 
0.9%
6.1 12
 
0.9%
5.9 5
 
0.4%
5.8 27
 
2.1%
5.7 28
2.2%
5.6 25
 
1.9%
5.5 68
5.2%
5.4 23
 
1.8%
5.3 27
 
2.1%
Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
4
669 
5
323 
3
308 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1300
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
4 669
51.5%
5 323
24.8%
3 308
23.7%

Length

2024-12-18T21:36:35.463907image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T21:36:35.990818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4 669
51.5%
5 323
24.8%
3 308
23.7%

Most occurring characters

ValueCountFrequency (%)
4 669
51.5%
5 323
24.8%
3 308
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 669
51.5%
5 323
24.8%
3 308
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 669
51.5%
5 323
24.8%
3 308
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 669
51.5%
5 323
24.8%
3 308
23.7%

Длительность
Real number (ℝ)

Distinct658
Distinct (%)50.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean601.89846
Minimum110
Maximum1079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:36.499772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile184
Q1405.5
median606
Q3806
95-th percentile997.05
Maximum1079
Range969
Interquartile range (IQR)400.5

Descriptive statistics

Standard deviation249.85629
Coefficient of variation (CV)0.41511369
Kurtosis-0.99301711
Mean601.89846
Median Absolute Deviation (MAD)200
Skewness-0.062817461
Sum782468
Variance62428.165
MonotonicityNot monotonic
2024-12-18T21:36:37.175729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 7
 
0.5%
449 7
 
0.5%
627 7
 
0.5%
788 6
 
0.5%
744 6
 
0.5%
503 6
 
0.5%
511 6
 
0.5%
684 6
 
0.5%
509 6
 
0.5%
324 6
 
0.5%
Other values (648) 1237
95.2%
ValueCountFrequency (%)
110 1
 
0.1%
121 4
0.3%
125 1
 
0.1%
129 2
0.2%
131 1
 
0.1%
132 1
 
0.1%
133 1
 
0.1%
134 1
 
0.1%
135 1
 
0.1%
136 3
0.2%
ValueCountFrequency (%)
1079 1
0.1%
1076 1
0.1%
1073 1
0.1%
1072 1
0.1%
1065 1
0.1%
1064 1
0.1%
1061 2
0.2%
1057 2
0.2%
1056 1
0.1%
1052 1
0.1%
Distinct42
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31980769
Minimum0
Maximum0.99
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:37.770417image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.17
median0.24
Q30.3
95-th percentile0.95
Maximum0.99
Range0.99
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.24984314
Coefficient of variation (CV)0.7812293
Kurtosis2.1774728
Mean0.31980769
Median Absolute Deviation (MAD)0.07
Skewness1.8953925
Sum415.75
Variance0.062421595
MonotonicityNot monotonic
2024-12-18T21:36:38.362599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0.24 93
 
7.2%
0.3 85
 
6.5%
0.28 83
 
6.4%
0.17 79
 
6.1%
0.25 72
 
5.5%
0.14 69
 
5.3%
0.21 64
 
4.9%
0.13 64
 
4.9%
0.16 62
 
4.8%
0.23 60
 
4.6%
Other values (32) 569
43.8%
ValueCountFrequency (%)
0 3
 
0.2%
0.11 31
 
2.4%
0.12 20
 
1.5%
0.13 64
4.9%
0.14 69
5.3%
0.15 49
3.8%
0.16 62
4.8%
0.17 79
6.1%
0.18 23
 
1.8%
0.19 11
 
0.8%
ValueCountFrequency (%)
0.99 30
2.3%
0.98 17
 
1.3%
0.95 24
1.8%
0.94 43
3.3%
0.93 19
1.5%
0.91 5
 
0.4%
0.9 11
 
0.8%
0.89 16
 
1.2%
0.75 1
 
0.1%
0.74 1
 
0.1%
Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.3 KiB
Товары для детей
330 
Домашний текстиль
251 
Косметика и аксесуары
223 
Техника для красоты и здоровья
184 
Мелкая бытовая техника и электроника
174 

Length

Max length36
Median length21
Mean length21.603077
Min length15

Characters and Unicode

Total characters28084
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowТовары для детей
2nd rowТовары для детей
3rd rowДомашний текстиль
4th rowТовары для детей
5th rowТовары для детей

Common Values

ValueCountFrequency (%)
Товары для детей 330
25.4%
Домашний текстиль 251
19.3%
Косметика и аксесуары 223
17.2%
Техника для красоты и здоровья 184
14.2%
Мелкая бытовая техника и электроника 174
13.4%
Кухонная посуда 138
10.6%

Length

2024-12-18T21:36:38.873815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-18T21:36:39.389223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
и 581
13.7%
для 514
12.2%
техника 358
 
8.5%
товары 330
 
7.8%
детей 330
 
7.8%
домашний 251
 
5.9%
текстиль 251
 
5.9%
косметика 223
 
5.3%
аксесуары 223
 
5.3%
красоты 184
 
4.4%
Other values (6) 982
23.2%

Most occurring characters

ValueCountFrequency (%)
2927
 
10.4%
а 2590
 
9.2%
е 2063
 
7.3%
о 1980
 
7.1%
и 1838
 
6.5%
к 1761
 
6.3%
т 1761
 
6.3%
с 1242
 
4.4%
я 1184
 
4.2%
д 1166
 
4.2%
Other values (19) 9572
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2927
 
10.4%
а 2590
 
9.2%
е 2063
 
7.3%
о 1980
 
7.1%
и 1838
 
6.5%
к 1761
 
6.3%
т 1761
 
6.3%
с 1242
 
4.4%
я 1184
 
4.2%
д 1166
 
4.2%
Other values (19) 9572
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2927
 
10.4%
а 2590
 
9.2%
е 2063
 
7.3%
о 1980
 
7.1%
и 1838
 
6.5%
к 1761
 
6.3%
т 1761
 
6.3%
с 1242
 
4.4%
я 1184
 
4.2%
д 1166
 
4.2%
Other values (19) 9572
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2927
 
10.4%
а 2590
 
9.2%
е 2063
 
7.3%
о 1980
 
7.1%
и 1838
 
6.5%
к 1761
 
6.3%
т 1761
 
6.3%
с 1242
 
4.4%
я 1184
 
4.2%
д 1166
 
4.2%
Other values (19) 9572
34.1%
Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.27
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:39.982973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3553504
Coefficient of variation (CV)0.41448023
Kurtosis-0.70012882
Mean3.27
Median Absolute Deviation (MAD)1
Skewness0.27330771
Sum4251
Variance1.8369746
MonotonicityNot monotonic
2024-12-18T21:36:40.773814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 356
27.4%
2 312
24.0%
4 263
20.2%
5 177
13.6%
1 106
 
8.2%
6 86
 
6.6%
ValueCountFrequency (%)
1 106
 
8.2%
2 312
24.0%
3 356
27.4%
4 263
20.2%
5 177
13.6%
6 86
 
6.6%
ValueCountFrequency (%)
6 86
 
6.6%
5 177
13.6%
4 263
20.2%
3 356
27.4%
2 312
24.0%
1 106
 
8.2%
Distinct11
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.84
Minimum0
Maximum10
Zeros116
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:41.193170image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9714514
Coefficient of variation (CV)0.69417302
Kurtosis0.45521388
Mean2.84
Median Absolute Deviation (MAD)1
Skewness0.76682589
Sum3692
Variance3.8866205
MonotonicityNot monotonic
2024-12-18T21:36:41.779101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 261
20.1%
1 261
20.1%
3 218
16.8%
4 197
15.2%
5 135
10.4%
0 116
8.9%
6 44
 
3.4%
7 34
 
2.6%
8 20
 
1.5%
9 10
 
0.8%
ValueCountFrequency (%)
0 116
8.9%
1 261
20.1%
2 261
20.1%
3 218
16.8%
4 197
15.2%
5 135
10.4%
6 44
 
3.4%
7 34
 
2.6%
8 20
 
1.5%
9 10
 
0.8%
ValueCountFrequency (%)
10 4
 
0.3%
9 10
 
0.8%
8 20
 
1.5%
7 34
 
2.6%
6 44
 
3.4%
5 135
10.4%
4 197
15.2%
3 218
16.8%
2 261
20.1%
1 261
20.1%

Ошибка_сервиса
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1853846
Minimum0
Maximum9
Zeros17
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:42.280383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9552976
Coefficient of variation (CV)0.46717275
Kurtosis-0.51036217
Mean4.1853846
Median Absolute Deviation (MAD)1
Skewness0.25214048
Sum5441
Variance3.8231888
MonotonicityNot monotonic
2024-12-18T21:36:42.763794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 257
19.8%
3 226
17.4%
5 208
16.0%
2 189
14.5%
6 156
12.0%
7 92
 
7.1%
1 74
 
5.7%
8 66
 
5.1%
0 17
 
1.3%
9 15
 
1.2%
ValueCountFrequency (%)
0 17
 
1.3%
1 74
 
5.7%
2 189
14.5%
3 226
17.4%
4 257
19.8%
5 208
16.0%
6 156
12.0%
7 92
 
7.1%
8 66
 
5.1%
9 15
 
1.2%
ValueCountFrequency (%)
9 15
 
1.2%
8 66
 
5.1%
7 92
 
7.1%
6 156
12.0%
5 208
16.0%
4 257
19.8%
3 226
17.4%
2 189
14.5%
1 74
 
5.7%
0 17
 
1.3%

Страниц_за_визит
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1769231
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:43.185822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q311
95-th percentile15
Maximum20
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9781261
Coefficient of variation (CV)0.48650648
Kurtosis-0.52970033
Mean8.1769231
Median Absolute Deviation (MAD)3
Skewness0.36781673
Sum10630
Variance15.825487
MonotonicityNot monotonic
2024-12-18T21:36:43.694234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 127
9.8%
5 115
8.8%
4 112
 
8.6%
8 109
 
8.4%
9 108
 
8.3%
10 104
 
8.0%
7 102
 
7.8%
11 92
 
7.1%
3 76
 
5.8%
12 73
 
5.6%
Other values (10) 282
21.7%
ValueCountFrequency (%)
1 20
 
1.5%
2 58
4.5%
3 76
5.8%
4 112
8.6%
5 115
8.8%
6 127
9.8%
7 102
7.8%
8 109
8.4%
9 108
8.3%
10 104
8.0%
ValueCountFrequency (%)
20 2
 
0.2%
19 5
 
0.4%
18 7
 
0.5%
17 19
 
1.5%
16 21
 
1.6%
15 36
 
2.8%
14 53
4.1%
13 61
4.7%
12 73
5.6%
11 92
7.1%
Distinct790
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4825.2069
Minimum0
Maximum5663
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:44.279335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4339
Q14583
median4809
Q35053.5
95-th percentile5442
Maximum5663
Range5663
Interquartile range (IQR)470.5

Descriptive statistics

Standard deviation405.97966
Coefficient of variation (CV)0.084137255
Kurtosis44.439422
Mean4825.2069
Median Absolute Deviation (MAD)237
Skewness-3.6751875
Sum6272769
Variance164819.49
MonotonicityNot monotonic
2024-12-18T21:36:45.070222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4974 6
 
0.5%
4940 5
 
0.4%
4690 5
 
0.4%
4908 5
 
0.4%
4648 5
 
0.4%
4795 5
 
0.4%
4729 5
 
0.4%
4833 5
 
0.4%
5241 5
 
0.4%
4952 5
 
0.4%
Other values (780) 1249
96.1%
ValueCountFrequency (%)
0 3
0.2%
4098 1
 
0.1%
4102 2
0.2%
4105 1
 
0.1%
4119 2
0.2%
4128 1
 
0.1%
4138 1
 
0.1%
4148 1
 
0.1%
4156 1
 
0.1%
4157 1
 
0.1%
ValueCountFrequency (%)
5663 1
0.1%
5653 1
0.1%
5641 1
0.1%
5638 1
0.1%
5637 1
0.1%
5633 1
0.1%
5624 1
0.1%
5621 1
0.1%
5618 1
0.1%
5616 1
0.1%

Выручка_предыдущий_месяц
Real number (ℝ)

High correlation 

Distinct1122
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4936.9204
Minimum0
Maximum6869.5
Zeros3
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:45.584916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3621.85
Q14496.75
median5005
Q35405.625
95-th percentile6039.8
Maximum6869.5
Range6869.5
Interquartile range (IQR)908.875

Descriptive statistics

Standard deviation739.598
Coefficient of variation (CV)0.14980959
Kurtosis3.985965
Mean4936.9204
Median Absolute Deviation (MAD)453.75
Skewness-0.93560657
Sum6417996.5
Variance547005.2
MonotonicityNot monotonic
2024-12-18T21:36:46.382052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5063 4
 
0.3%
5277.5 4
 
0.3%
5051 4
 
0.3%
0 3
 
0.2%
4914 3
 
0.2%
4729 3
 
0.2%
5118.5 3
 
0.2%
5005.5 3
 
0.2%
5063.5 3
 
0.2%
4828 3
 
0.2%
Other values (1112) 1267
97.5%
ValueCountFrequency (%)
0 3
0.2%
2890 1
 
0.1%
2909 1
 
0.1%
2960 1
 
0.1%
2970 1
 
0.1%
3061 1
 
0.1%
3067 1
 
0.1%
3111 2
0.2%
3130 2
0.2%
3137 1
 
0.1%
ValueCountFrequency (%)
6869.5 1
0.1%
6809 1
0.1%
6716.5 1
0.1%
6658.5 1
0.1%
6604 1
0.1%
6588.5 1
0.1%
6531 1
0.1%
6499 1
0.1%
6457 1
0.1%
6407.5 1
0.1%

Выручка_текущий_месяц
Real number (ℝ)

High correlation  Skewed 

Distinct1242
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5314.9608
Minimum2758.7
Maximum106862.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:46.986084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2758.7
5-th percentile3877.66
Q14706.05
median5179.95
Q35761.725
95-th percentile6722.2
Maximum106862.2
Range104103.5
Interquartile range (IQR)1055.675

Descriptive statistics

Standard deviation2939.7082
Coefficient of variation (CV)0.55310062
Kurtosis1098.178
Mean5314.9608
Median Absolute Deviation (MAD)523.15
Skewness31.777248
Sum6909449.1
Variance8641884
MonotonicityNot monotonic
2024-12-18T21:36:47.575788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4721.5 3
 
0.2%
5128.4 3
 
0.2%
5339.6 3
 
0.2%
5341.8 2
 
0.2%
4903.1 2
 
0.2%
4977.8 2
 
0.2%
5349.4 2
 
0.2%
6118.6 2
 
0.2%
5010.9 2
 
0.2%
5854.2 2
 
0.2%
Other values (1232) 1277
98.2%
ValueCountFrequency (%)
2758.7 1
0.1%
2952.2 1
0.1%
3078.3 1
0.1%
3083.7 1
0.1%
3085.4 1
0.1%
3143.5 1
0.1%
3227 1
0.1%
3232.2 1
0.1%
3237.2 1
0.1%
3267 1
0.1%
ValueCountFrequency (%)
106862.2 1
0.1%
7799.4 1
0.1%
7605.3 1
0.1%
7557 1
0.1%
7547.8 1
0.1%
7467.9 1
0.1%
7410 1
0.1%
7401.6 2
0.2%
7374 1
0.1%
7370.6 1
0.1%

Время_в_предыдущем_месяце_мин
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.467692
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:48.071323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q111
median13
Q317
95-th percentile20
Maximum23
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.9320286
Coefficient of variation (CV)0.29196009
Kurtosis-0.72579117
Mean13.467692
Median Absolute Deviation (MAD)3
Skewness0.093878289
Sum17508
Variance15.460849
MonotonicityNot monotonic
2024-12-18T21:36:48.578975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
12 119
 
9.2%
14 118
 
9.1%
11 115
 
8.8%
13 110
 
8.5%
15 106
 
8.2%
10 100
 
7.7%
17 93
 
7.2%
16 81
 
6.2%
18 81
 
6.2%
9 77
 
5.9%
Other values (9) 300
23.1%
ValueCountFrequency (%)
5 11
 
0.8%
6 24
 
1.8%
7 43
 
3.3%
8 69
5.3%
9 77
5.9%
10 100
7.7%
11 115
8.8%
12 119
9.2%
13 110
8.5%
14 118
9.1%
ValueCountFrequency (%)
23 6
 
0.5%
22 10
 
0.8%
21 26
 
2.0%
20 50
3.8%
19 61
4.7%
18 81
6.2%
17 93
7.2%
16 81
6.2%
15 106
8.2%
14 118
9.1%
Distinct20
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.204615
Minimum4
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.3 KiB
2024-12-18T21:36:49.185859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7
Q110
median13
Q316
95-th percentile20.05
Maximum23
Range19
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2205892
Coefficient of variation (CV)0.31962985
Kurtosis-0.67398316
Mean13.204615
Median Absolute Deviation (MAD)3
Skewness0.21721063
Sum17166
Variance17.813374
MonotonicityNot monotonic
2024-12-18T21:36:49.793437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
14 138
10.6%
12 118
 
9.1%
9 109
 
8.4%
11 106
 
8.2%
10 104
 
8.0%
13 96
 
7.4%
16 81
 
6.2%
8 72
 
5.5%
17 70
 
5.4%
15 68
 
5.2%
Other values (10) 338
26.0%
ValueCountFrequency (%)
4 9
 
0.7%
5 11
 
0.8%
6 35
 
2.7%
7 45
 
3.5%
8 72
5.5%
9 109
8.4%
10 104
8.0%
11 106
8.2%
12 118
9.1%
13 96
7.4%
ValueCountFrequency (%)
23 8
 
0.6%
22 30
 
2.3%
21 27
 
2.1%
20 51
 
3.9%
19 58
4.5%
18 64
4.9%
17 70
5.4%
16 81
6.2%
15 68
5.2%
14 138
10.6%

Interactions

2024-12-18T21:36:22.571321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:19.384865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:24.699269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:29.895556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:35.285183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:40.584912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:45.807587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:51.095788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:56.285028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:01.489155image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:06.791053image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:11.979197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:17.200160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:22.980312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:19.808453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:25.095042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:30.267785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:35.696807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:40.986846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:46.167540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:51.466042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:56.689976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:01.876365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:07.189569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:12.360960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:17.586816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:23.390840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:20.184945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:25.470394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:30.675746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:36.101762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:41.404862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:46.599982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:51.871300image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:57.107758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:02.282709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:07.562406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:12.756258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:17.992074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:23.803994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:20.688149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:25.888525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:31.069259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:36.508480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:41.774566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:46.978756image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:52.280803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:57.496182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:02.684727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:07.980699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:13.098813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:18.381903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:24.178969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:21.081321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:26.296558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:31.456282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:36.935673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:42.199484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:47.363375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:52.686823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:57.911162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:03.077079image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:08.386108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:13.504405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:19.007483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:24.584284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:21.480332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:26.680106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:31.865902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:37.375412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:42.590196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:47.757122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:53.086541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:58.296237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:03.493719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:08.785371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:13.884591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:19.391586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:24.972202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:21.886402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:27.086240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:32.283660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:37.787420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:42.973680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:48.184484image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:53.494576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:58.703074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:03.888361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:09.191689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:14.277669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:19.791975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:25.366338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:22.263308image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:27.479198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:32.692996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:38.159346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:43.357566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:48.561232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:53.881535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:59.064182image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:04.280234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:09.590781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:14.688273image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:20.180263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:25.794393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:22.686969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:27.872052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:33.085598image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:38.590711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:43.785555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:48.965357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:54.294833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:59.461445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:04.693947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:09.976711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:15.087914image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:20.607621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:26.170882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:23.090009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:28.280190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:33.485831image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:38.966824image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:44.183244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:49.382501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:54.684703image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:59.888672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:05.182391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:10.392636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:15.495144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:20.980935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:26.577775image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:23.492347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:28.691286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:33.869712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:39.395516image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:44.602113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:49.805055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:55.077960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:00.297044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:05.583355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:10.779299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:15.974597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:21.377889image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:26.969597image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:23.884951image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:29.091546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:34.283374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:39.770973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:44.986267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:50.293543image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:55.485458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:00.678549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:05.964853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:11.178705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:16.376895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:21.767787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:27.391559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:24.297786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:29.518235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:34.859034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:40.184550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:45.399160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:50.669863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:35:55.905005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:01.104003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:06.390921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:11.592727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:16.794028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-12-18T21:36:22.171041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-12-18T21:36:50.222992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
idАкционные_покупкиВремя_в_предыдущем_месяце_минВремя_в_текущем_месяце_минВыручка_предыдущий_месяцВыручка_препредыдущий_месяцВыручка_текущий_месяцДлительностьМаркет_актив_6_месМаркет_актив_тек_месНеоплаченные_продукты_штук_кварталОшибка_сервисаПокупательская активностьПопулярная_категорияРазрешить сообщатьСредний_просмотр_категорий_за_визитСтраниц_за_визитТип сервиса
id1.000-0.3700.4860.434-0.0720.346-0.095-0.0550.3410.000-0.2550.0980.8890.0920.0000.2760.5300.091
Акционные_покупки-0.3701.000-0.331-0.2710.014-0.2590.0260.036-0.2750.0040.198-0.0510.4990.0510.000-0.205-0.3650.046
Время_в_предыдущем_месяце_мин0.486-0.3311.0000.361-0.0840.348-0.144-0.1230.2740.063-0.1830.1950.5300.1070.0400.1480.4510.097
Время_в_текущем_месяце_мин0.434-0.2710.3611.000-0.0800.301-0.1650.0850.2260.038-0.2150.0750.4440.0480.0000.2330.2990.000
Выручка_предыдущий_месяц-0.0720.014-0.084-0.0801.0000.3130.8770.020-0.0040.0550.0340.0350.1700.1330.000-0.052-0.0630.033
Выручка_препредыдущий_месяц0.346-0.2590.3480.3010.3131.0000.152-0.0320.2340.054-0.2480.0900.3240.0000.0000.1460.3150.060
Выручка_текущий_месяц-0.0950.026-0.144-0.1650.8770.1521.0000.0170.0000.0000.0620.0440.0000.0290.000-0.073-0.0490.000
Длительность-0.0550.036-0.1230.0850.020-0.0320.0171.000-0.0430.107-0.1080.1030.0690.0000.190-0.046-0.0300.311
Маркет_актив_6_мес0.341-0.2750.2740.226-0.0040.2340.000-0.0431.0000.046-0.1340.0390.4200.0790.0000.1840.3200.071
Маркет_актив_тек_мес0.0000.0040.0630.0380.0550.0540.0000.1070.0461.0000.0870.0690.0000.0550.0870.1050.0000.074
Неоплаченные_продукты_штук_квартал-0.2550.198-0.183-0.2150.034-0.2480.062-0.108-0.1340.0871.000-0.0970.3860.1330.150-0.252-0.1650.093
Ошибка_сервиса0.098-0.0510.1950.0750.0350.0900.0440.1030.0390.069-0.0971.0000.1680.0000.0000.0080.1040.056
Покупательская активность0.8890.4990.5300.4440.1700.3240.0000.0690.4200.0000.3860.1681.0000.2130.0000.3870.5880.084
Популярная_категория0.0920.0510.1070.0480.1330.0000.0290.0000.0790.0550.1330.0000.2131.0000.0510.0830.0840.048
Разрешить сообщать0.0000.0000.0400.0000.0000.0000.0000.1900.0000.0870.1500.0000.0000.0511.0000.0520.0340.185
Средний_просмотр_категорий_за_визит0.276-0.2050.1480.233-0.0520.146-0.073-0.0460.1840.105-0.2520.0080.3870.0830.0521.0000.2650.130
Страниц_за_визит0.530-0.3650.4510.299-0.0630.315-0.049-0.0300.3200.000-0.1650.1040.5880.0840.0340.2651.0000.079
Тип сервиса0.0910.0460.0970.0000.0330.0600.0000.3110.0710.0740.0930.0560.0840.0480.1850.1300.0791.000

Missing values

2024-12-18T21:36:27.984050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-18T21:36:29.001822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idПокупательская активностьТип сервисаРазрешить сообщатьМаркет_актив_6_месМаркет_актив_тек_месДлительностьАкционные_покупкиПопулярная_категорияСредний_просмотр_категорий_за_визитНеоплаченные_продукты_штук_кварталОшибка_сервисаСтраниц_за_визитВыручка_препредыдущий_месяцВыручка_предыдущий_месяцВыручка_текущий_месяцВремя_в_предыдущем_месяце_минВремя_в_текущем_месяце_мин
0215348Снизиласьпремиумда3.451210.00Товары для детей62150.00.03293.113.014.0
1215349Снизиласьпремиумда4.448190.75Товары для детей44254472.05216.04971.612.010.0
2215350Снизиласьстандартнет4.935390.14Домашний текстиль52154826.05457.55058.48.013.0
3215351Снизиласьстандартда3.258960.99Товары для детей50644793.06158.06610.411.013.0
4215352Снизиласьстандартнет5.1310640.94Товары для детей32324594.05807.55872.58.011.0
5215353Снизиласьстандартда3.347620.26Домашний текстиль41145124.04738.55388.510.010.0
6215354Снизиласьстандартда5.134310.23Косметика и аксесуары23724503.05685.05869.611.012.0
7215355Снизиласьстандартнет4.742840.17Товары для детей51644749.03263.03772.612.010.0
8215356Снизиласьстандартда4.241920.14Косметика и аксесуары22134433.04146.54566.46.07.0
9215357Снизиласьстандартда3.951540.00Техника для красоты и здоровья33950.00.05986.312.06.0
idПокупательская активностьТип сервисаРазрешить сообщатьМаркет_актив_6_месМаркет_актив_тек_месДлительностьАкционные_покупкиПопулярная_категорияСредний_просмотр_категорий_за_визитНеоплаченные_продукты_штук_кварталОшибка_сервисаСтраниц_за_визитВыручка_препредыдущий_месяцВыручка_предыдущий_месяцВыручка_текущий_месяцВремя_в_предыдущем_месяце_минВремя_в_текущем_месяце_мин
1290216638Прежний уровеньстандартнет1.539300.29Мелкая бытовая техника и электроника204164613.03234.04557.411.014.0
1291216639Прежний уровеньстандартда4.843060.29Товары для детей45374591.04648.04827.414.022.0
1292216640Прежний уровеньстандартнет5.744160.95Кухонная посуда235134679.04212.53938.223.017.0
1293216641Прежний уровеньстандартда4.146380.22Техника для красоты и здоровья416145176.05654.56199.214.012.0
1294216642Прежний уровеньпремиумда4.239910.40Мелкая бытовая техника и электроника435125011.04589.54354.219.019.0
1295216643Прежний уровеньстандартда6.633180.24Техника для красоты и здоровья533114704.03664.04741.714.07.0
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